"New Brainlike Computers, Learning From Experience," reads a headline on the front page of The New York Times this morning. The article focuses on machine-learning algorithms, known as a neural networks, that are becoming increasingly important in computer science. And in 2014 Qualcomm will release the first commercial version of a neuromorphic processor that transforms this software technique directly into hardware to increase performance for intensive machine learning tasks.

But buried in the last paragraph of the story was the fact that "The largest class on campus this fall at Stanford was a graduate level machine-learning course covering both statistical and biological approaches, taught by the computer scientist Andrew Ng. More than 760 students enrolled." And several previous versions of the course are available online for free. The most recent is from Coursera (which Ng cofounded with Daphne Koller last year) but the 2008 course is on iTune U, YouTube and Stanford's Engineering Everywhere.

What's going on here? Simply put, machine learning is the part of artificial intelligence that actually works. You can use it to train computers to do things that are impossible to program in advance. Ng uses the example of handwriting recognition as a classic example of a problem that can only be achieved through machine learning. In his introductory lecture on Coursera, Ng refers to search engines like Google and Bing, Facebook and Apple's photo tagging application and Gmail's spam filtering as everyday examples of machine learning at work. Ng is the director of the Stanford Artificial Intelligence Lab and one of the founders, with Jeff Dean, of Google Brain, a deep learning research project at Google. He is using machine learning as a step towards the "AI dream of someday building machines as intelligent as you or I."

It turns out that artificial intelligence and the robotics that is tied to it, consists of two primary systems, control and perception. There has been much progress with control (as evidenced by the clutch of robotics companies Google has acquired of late), but perception has been more difficult. Most approaches, going back to Marvin Minsky's classic Society of Mind, have tried to model all of the different functions required by a given mode of perception (visual, audio and touch) and wire these "agents" all together. Ng tried this approach himself for many years before he came across the work of Jeff Hawkins in 2006.

Hawkins theorized, in Ng's words, that "Most of perception in the brain may be one simple program." In other words, the same algorithm in the brain can perceive differences in visual, auditory and tactile inputs. This is the basis of "deep learning" and holds the most promise for making rapid progress in the perceptual aspect of AI that has been holding back true machine intelligence. See Ng's description in the video below on The Future of Robotics and Artificial Intelligence (the link above is to the specific point in the video that he discusses Hawkins' "one algorithm" theory):

The New York Times article points to a number of other research efforts relating to machine learning and neural networks. Stanford researcher Kwabena Boahen leads the Brains in Silicon program that is working on "a fundamental, biological understanding of how the brain works." Boahen tells the Times that this kind of functional understanding is key, “I’m an engineer, and I build things. There are these highfalutin theories, but give me one that will let me build something.”

Neural networks wire together many processors and the connections between the processors are "weighted" based on data that is input. As more data is added to the system these weightings change and, in effect, the new configuration is a form of learning. “Instead of bringing data to computation as we do today, we can now bring computation to data,” Dharmendra Modha, a cognitive computing researcher at I.B.M. tells the Times. I.B.M. has built a supercomputer that simulates a brain with 10 billion neurons (a tenth of those in a human brain.)

Meanwhile, Ng's group at Google built an algorithms that learned to recognize cats from sampling millions of (cute) cat images on the internet without having any prior concept of a "cat." This is just the sort of emergent classifier that Hawkins' "one algorithm" theory would predict. Ng recently told Wired, that the Deep Learning process involves giving the system lots of data, “so it can discover by itself what some of the concepts in the world are.”

For robots to act autonomously and intelligently and for other forms of technology to function unobtrusively in the world, this kind of machine learning is essential. It is no wonder that Stanford students can't get enough of it.